glmmPQL inquiry

Hi Ken,
With my dataset that has 50 subjects each with three observations, i  
get the error i gave if i use the compound symmetry covariance  
structure. Would you have an idea why this is happening?

Kennedy

----- Original Message ----
From: Ken Beath <kjbeath at kagi.com>
To: kennedy otwombe <notwombe at yahoo.com>
Cc: R-SIG-Mixed-Models <R-sig-mixed-models at r-project.org>
Sent: Friday, December 7, 2007 11:30:15 AM
Subject: Re: [R-sig-ME] glmmPQL inquiry

On 07/12/2007, at 7:58 PM, kennedy otwombe wrote:

Hi Sundar/Ken,
I would like to clarify my inquiry. The data structure i gave was
just a sample of my data. I actually have 50 subjects each with
three binary observations (hence 150 observations). So the error i
initially gave emanates from the analysis involving all the 50
subjects. It just turns out that for the sample i gave, the first
two subjects have only 0 as their entries but this varies as you
look through the data.
I am using the latest version of R i.e 2.6.1 and i downloaded the
nlme version currently available on the CRAN website. I am aslo
assuming my random part is the intercept.
Hope this clarifies my inquiry.

If I modify your code to include better values for y, using code

library(MASS)
navs<-data.frame(matrix(c(1,1,1,0,0,1,1,
2,1,0,0,1,0,1,
3,1,0,0,1,0,1,
1,2,1,0,0,1,0,
2,2,1,1,1,1,0,
3,2,0,1,1,1,0),nrow=6,byrow=T))

names(navs) <- c("t","id","y","x0","x1","x2","x3")

fit<-glmmPQL(y~x1+x2+x3, random=~1|id, family=binomial, data=navs)
summary(fit)

the results are fine, except for some problems due to not having
enough subjects, as in the following output.

Ken

library(MASS)
navs<-data.frame(matrix(c(1,1,1,0,0,1,1,

+ 2,1,0,0,1,0,1,
+ 3,1,0,0,1,0,1,
+ 1,2,1,0,0,1,0,
+ 2,2,1,1,1,1,0,
+ 3,2,0,1,1,1,0),nrow=6,byrow=T))

names(navs) <- c("t","id","y","x0","x1","x2","x3")

fit<-glmmPQL(y~x1+x2+x3, random=~1|id, family=binomial, data=navs)

iteration 1
iteration 2
iteration 3
iteration 4
iteration 5
iteration 6
iteration 7
iteration 8
iteration 9
iteration 10

summary(fit)

Linear mixed-effects model fit by maximum likelihood
 Data: navs
 AIC BIC logLik
   NA  NA    NA

Random effects:
 Formula: ~1 | id
         (Intercept)  Residual
StdDev: 8.009601e-05 0.5773503

Variance function:
 Structure: fixed weights
 Formula: ~invwt
Fixed effects: y ~ x1 + x2 + x3
                 Value Std.Error DF      t-value p-value
(Intercept)  0.000353  6171979  2  5.700000e-11      1
x1          -30.566351  2631792  2 -1.161427e-05      1
x2          30.565997  4161051  2  7.345739e-06      1
x3          -0.000071  3721849  0 -1.900000e-11    NaN
 Correlation:
   (Intr) x1    x2
x1 -0.853
x2 -0.944  0.632
x3 -0.905  0.707  0.894

Standardized Within-Group Residuals:
         Min            Q1          Med            Q3          Max
-1.732051e+00 -5.451298e-17  8.078053e-16  1.506878e-15  1.732051e+00

Number of Observations: 6
Number of Groups: 2
Warning message:
In pt(q, df, lower.tail, log.p) : NaNs produced